Accurate and reliable load forecasting is necessary to ameliorate energy management. Short-term load forecast plays a crucial role in economic and secure system operation. This paper presents a practical method for short-term electric load forecast problem using an artificial neural network with a powerful Levenberg-Marquardt training algorithm approach. The applications of real load from Ontario, Canada with hourly load, daily load, and weekly load predictions have been successfully achieved. Both visual comparison and statistical test are discussed and analyzed to validate training and testing phases of the neural network.